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Mobile Information Systems
Volume 2017 (2017), Article ID 2409830, 10 pages
https://doi.org/10.1155/2017/2409830
Research Article

Sliding Window Based Feature Extraction and Traffic Clustering for Green Mobile Cyberphysical Systems

1College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China
2Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang, China
4Department of Mechanical Engineering Technology, New York City College of Technology, City University of New York, Brooklyn, NY 11201, USA
5IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA

Correspondence should be addressed to Li Zhou; nc.ude.tdun@5302iluohz

Received 16 February 2017; Accepted 4 May 2017; Published 30 May 2017

Academic Editor: Jun Cheng

Copyright © 2017 Jiao Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. F. Monserrat, I. Alepuz, J. Cabrejas et al., “Towards user-centric operation in 5G networks,” Eurasip Journal on Wireless Communications and Networking, vol. 2016, no. 1, article no. 6, pp. 1–7, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. J. F. Monserrat, G. Mange, V. Braun, H. Tullberg, G. Zimmermann, and Ö. Bulakci, “METIS research advances towards the 5G mobile and wireless system definition,” Eurasip Journal on Wireless Communications and Networking, vol. 2015, no. 1, pp. 1–16, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Zhou, Z. Sheng, L. Wei et al., “Green cell planning and deployment for small cell networks in smart cities,” Ad Hoc Networks, vol. 43, pp. 30–42, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Zhou, C. Zhu, R. Ruby et al., “QoS-aware energy-efficient resource allocation in OFDM-based heterogenous cellular networks,” International Journal of Communication Systems, vol. 30, no. 2, p. e2931, 2017. View at Publisher · View at Google Scholar
  5. M. Hoshino, Y. Yuda, T. Takata, and A. Nishio, “Performance evaluation of jt-comp under non full buffer traffic condition on heterogeneous network with dense small cells,” vol. 112, pp. 29–34, 2012. View at Google Scholar
  6. L. Zhou, X. Hu, E. C.-H. Ngai et al., “A dynamic graph-based scheduling and interference coordination approach in heterogeneous cellular networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 3735–3748, 2016. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Mirahsan, Z. Wang, R. Schoenen, H. Yanikomeroglu, and M. St-Hilaire, “Unified and non-parameterized statistical modeling of temporal and spatial traffic heterogeneity in wireless cellular networks,” in Proceedings of 2014 IEEE International Conference on Communications Workshops, ICC 2014, pp. 55–60, aus, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. ITU-R, “Guidelines for evaluation of radio interface technologies for imt-advanced,” M.2135-1, 2009.
  9. J. Rataj, I. Saxl, and K. Pelikán, “Convergence of randomly oscillating point patterns to the Poisson point process,” Applications of Mathematics, vol. 38, no. 3, pp. 221–235, 1993. View at Google Scholar
  10. V. Lucarini, “From symmetry breaking to Poisson point process in 2D Voronoi tessellations: the generic nature of hexagons,” Journal of Statistical Physics, vol. 130, no. 6, pp. 1047–1062, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. B. Błaszczyszyn and D. Yogeshwaran, “Clustering comparison of point processes, with applications to random geometric models,” Lecture Notes in Mathematics, vol. 2120, pp. 31–71, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Mirahsan, R. Schoenen, and H. Yanikomeroglu, “HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 10, pp. 2252–2265, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. K. Son, S. Nagaraj, M. Sarkar, and S. Dey, “QoS-aware dynamic cell reconfiguration for energy conservation in cellular networks,” in Proceedings of 2013 IEEE Wireless Communications and Networking Conference, WCNC 2013, pp. 2022–2027, chn, April 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Lorincz, A. Capone, and D. Begušić, “Optimized network management for energy savings of wireless access networks,” Computer Networks, vol. 55, no. 3, pp. 514–540, 2011. View at Publisher · View at Google Scholar
  15. H.-S. Jung, H.-T. Roh, and J.-W. Lee, “Energy and traffic aware dynamic topology management for wireless cellular networks,” in Proceedings of 2012 IEEE International Conference on Communication Systems, ICCS 2012, pp. 205–209, sgp, November 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Su, E. Sun, M. Li, F. R. Yu, and Y. Zhang, “An Energy-Efficient User Location-Aware Switch-Off Method for LTE-A Cellular Networks,” Wireless Personal Communications, vol. 84, no. 3, pp. 1817–1833, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Al-Kanj, W. El-Beaino, A. M. El-Hajj, and Z. Dawy, “Optimized joint cell planning and BS on/off switching for LTE networks,” Wireless Communications and Mobile Computing, vol. 16, no. 12, pp. 1537–1555, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. X. Hu, T. H. S. Chu, H. C. B. Chan, and V. C. M. Leung, “Vita: a crowdsensing-oriented mobile cyber-physical system,” IEEE Transactions on Emerging Topics in Computing, vol. 1, no. 1, pp. 148–165, 2013. View at Publisher · View at Google Scholar
  19. X. Hu, T. H. S. Chu, V. C. M. Leung, and C. H. Ngai, “A survey on mobile social networks: Applications, platforms, system architectures, and future research directions,” IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1557–1581, 2014. View at Google Scholar
  20. C.-L. Fok, M. Hanna, S. Gee et al., “A platform for evaluating autonomous intersection management policies,” in Proceedings of IEEE/ACM Third International Conference on Cyber-Physical Systems, pp. 87–96, Beijing, China, April 2012. View at Publisher · View at Google Scholar
  21. S. A. Haque, S. M. Aziz, and M. Rahman, “Review of cyber-physical system in healthcare,” International Journal of Distributed Sensor Networks, vol. 2014, Article ID 217415, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Sztipanovits, “Composition of cyber-physical systems,” in Proceedings of 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, ECBS 2007, pp. 3-4, usa, March 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. E. A. Lee, “Cyber physical systems: design challenges,” in Proceedings of the 11th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC '08), pp. 363–369, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Perumal, P. Rajasekaran, and M. H. M. Ramalingam, “Wsn integrated cloud for automated telemedicine (atm) based e-healthcare applications,” in Proceedings of International Proceedings of Chemical Biological & Environmenta, 2012.
  25. X. Hu, J. Zhao, B.-C. Seet, V. C. M. Leung, T. H. S. Chu, and H. Chan, “S-aframe: agent-based multilayer framework with context-aware semantic service for vehicular social networks,” IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 1, pp. 44–63, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. X. Hu, X. Li, E. C.-H. Ngai, V. C. M. Leung, and P. Kruchten, “Multidimensional context-aware social network architecture for mobile crowdsensing,” IEEE Communications Magazine, vol. 52, no. 6, pp. 78–87, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Zhou, J. Zhang, B. Seet et al., “Software Defined Small Cell Networking under Dynamic Traffic Patterns,” in Proceedings of IEEE Cyber Science and Technology Congress, Auckland, New Zealand, August 2016. View at Publisher · View at Google Scholar
  28. A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264–323, 1999. View at Publisher · View at Google Scholar · View at Scopus
  29. B. S. Everitt, G. Dunn, B. S. Everitt, and G. Dunn, “Cluster analysis,” 1em plus 0.5em minus 0.4em Wiley, 2011.
  30. A. K. Jain and R. C. Dubes, “Algorithms for clustering data,” Technometrics, vol. 32, no. 2, pp. 227–229, 1988. View at Google Scholar
  31. E. W. Forgy, “Cluster analysis of multivariate data efficiency vs. interpretability of classification,” Biometrics, vol. 21, no. 3, pp. 41–52, 1965. View at Google Scholar
  32. J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297, University of California Press, Berkeley, California, 1967. View at MathSciNet
  33. V. Estivill-Castro and J. Yang, “A fast and robust general purpose clustering algorithm,” in Proceedings of the Pacific Rim International Conference on Artificial Intelligence, pp. 208–218, Springer-Verlag, London, UK, 1999.
  34. B. Schölkopf, A. Smola, and K.-R. Müller, “Nonlinear component analysis as a kernel eigenvalue problem,” Neural Computation, vol. 10, no. 5, pp. 1299–1319, 1998. View at Publisher · View at Google Scholar
  35. U. von Luxburg, “A tutorial on spectral clustering,” Statistics and Computing, vol. 17, no. 4, pp. 395–416, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  36. A. Y. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering Analysis and an algorithm,” in Proceedings of the Advances in Neural Information Processing Systems, vol. 14, pp. 849–856, San Francisco, CA, USA, 2002.
  37. P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, no. 20, pp. 53–65, 1987. View at Publisher · View at Google Scholar
  38. L. Kaufman and P. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, New York, NY, USA, 1990. View at Publisher · View at Google Scholar · View at MathSciNet